Substantial increase in perfluorocarbons CF4 (PFC-14) and C2F6 (PFC-116) emissions in China

Significance We investigate the emissions of two potent greenhouse gases, perfluorocarbons tetrafluoromethane (CF4, PFC-14) and hexafluoroethane (C2F6, PFC-116), in China. Based on atmospheric observations within China, we report substantial increases in CF4 and C2F6 emissions in China over the last decade. These increases in national emissions are sufficient to explain the entire increases in global emissions over the same period. We suggest that substantial CF4 and C2F6 emissions could be due to by-product emissions from the aluminum industry in the less populated and less economically developed western regions in China. The findings highlight the importance of mitigating CF4 and C2F6 emissions in China and provide guidance for directing mitigation strategies toward specific regions and/or industries.

The perfluorocarbons (PFCs) are potent greenhouse gases which previously had their emissions regulated under the Kyoto Protocol and are currently included in the nonbinding Paris Agreement (1)(2)(3).Tetrafluoromethane (CF 4 , PFC-14) and hexafluoroethane (C 2 F 6 , PFC-116), with atmospheric lifetimes of 50,000 and 10,000 y, respectively, are the two most abundant PFCs in the atmosphere (4)(5)(6).They have large global warming potentials over a 100-y time horizon (GWP 100 ), of 7,380 for CF 4 and 12,400 for C 2 F 6 (6).Due to their exceptionally long atmospheric lifetimes, emissions of CF 4 and C 2 F 6 alter the global radiative budget essentially permanently compared to human time scales.Substantial by-product emissions of CF 4 and C 2 F 6 are created during metal smelting (mainly aluminum) due to anode effects (see refs.7-10 and references therein), causing the majority of their historic anthropogenic emissions.CF 4 and C 2 F 6 have also been used extensively as plasma etching gases in the semiconductor and flat-panel display industries, where release to the atmosphere can occur if the unused gas is not recovered or properly destroyed (7)(8)(9)(10).
Measurements of CF 4 and C 2 F 6 (from air trapped in ice cores, firn air, archived air, and in situ) have shown preindustrial CF 4 abundances of ~34.1 ppt (19th century) due to a very small natural source, but less than 0.002 ppt of C 2 F 6 (11).Global background mole fractions of both CF 4 and C 2 F 6 have been increasing due to anthropogenic activities since the early 1900s, with a rapid increase around 1940 due to the aluminum demand during World War II (11,12).The global average mole fraction reached ~86 ppt for CF 4 and ~4.9 ppt for C 2 F 6 in 2020 according to observations from the Advanced Global Atmospheric Gases Experiment (AGAGE) (4).Global emissions of CF 4 and C 2 F 6 , derived using global background measurements and an inverse modeling method, have increased steadily since the 2010s (4,12), after a period of decreasing emissions of both substances since their emission peaks in ~1980 for CF 4 and ~2000 for C 2 F 6 (11)(12)(13).These recent increases in emissions are occurring despite efforts from the semiconductor and aluminum industries to reduce the emissions in recent decades (7,9,(14)(15)(16).Combined global CF 4 and C 2 F 6 emissions reached 138 Mt CO 2eq (15 Gg y −1 of CF 4 and 2.2 Gg y −1 of C 2 F 6 ) in 2020 (4,12).A previous study suggested that the effectiveness of emission reduction measures implemented by the semiconductor and flat-panel display industries may be overestimated and attributed the global increase of CF 4 and C 2 F 6 in recent years to the emissions from expanding aluminum and semiconductor industries in East Asia, including China (9).
Emissions of PFCs in China from the expanding aluminum industry [especially due to low voltage anode effects (9,(16)(17)(18) and continuous emissions (16)] and perhaps from the rare earth metal industry (which uses similar processes) (9,19), as well as from the semiconductor and flat-panel display industries, are important to understand the global CF 4 and C 2 F 6 budget.Several previous studies have focused on the emissions of CF 4 and C 2 F 6 in China.Large discrepancies exist between different bottom-up (industry data-based) emission inventories (8,9,(20)(21)(22)(23)(24)(25)(26) and top-down (atmospheric observation-based) emission estimates (7,9,(27)(28)(29)(30) in China.Topdown emission estimates based on atmospheric observations can provide useful information to evaluate and improve national bottom-up inventories (17).However, existing long-term top-down estimates of CF 4 and C 2 F 6 emissions in China are based on measurements made outside of China (specifically in South Korea) (7,9), which lack sensitivity to the western regions of China.
In this study, the emissions of CF 4 and C 2 F 6 in China (not including emissions from Hong Kong, Macao, Taiwan, and the ocean areas, throughout this study) over 2011 to 2021 were derived using long-term atmospheric observations from nine sites within China and a top-down inverse modeling technique.The measurement sites are distributed throughout China and provide good sensitivity to emissions across China.The derived spatial distributions of emissions in China are used to investigate the potential source sectors for the emissions.Finally, CF 4 and C 2 F 6 emission increases in China are discussed in the context of the increases in global emissions.

Results
Emissions of CF 4 and C 2 F 6 in China.Emissions of both CF 4 and C 2 F 6 in China derived in this study show substantial increases over 2011 to 2021 (Fig. 1).Emissions of CF 4 increased from 4.7 (4.2-5.0,68% uncertainty interval, the same hereafter) Gg y −1 in 2012 to 8.3 (7.7-8.9)Gg y −1 in 2021, a relative increase of ~78%.Emissions of C 2 F 6 increased from 0.74 (0.66-0.80)Gg y −1 in 2011 to 1.32 (1.24-1.40)Gg y −1 in 2021, a relative increase of ~78%.These derived (posterior) emissions of CF 4 and C 2 F 6 are relatively insensitive to the a priori emissions magnitudes and prior probability distributions (SI Appendix, Fig. S1) and also to the choices of measurement datasets (SI Appendix, Fig. S2) that were used in the inversion, both in terms of the a posteriori emission magnitudes in each specific year and the general trend.
Several previous studies (7)(8)(9)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) have quantified emissions of CF 4 and C 2 F 6 in China, and substantial increases in CF 4 and C 2 F 6 emissions have been identified in some of these studies [e.g., Guo et al. (8) and Kim et al. ( 9)] (Fig. 1).However, large discrepancies exist between previous bottom-up and top-down emissions.The top-down CF 4 and C 2 F 6 emissions derived in this study agree reasonably with previously published top-down emissions, given some of the large uncertainties (7,9).The relative differences between previous top-down studies (7,9) and the mean estimates of this study (~24% for CF 4 emissions and ~11% for C 2 F 6 emissions, on average over the overlapping years) may result from the different atmospheric measurements used and perhaps also from the different inverse modeling approaches taken.These top-down emission time series [from this study and previous studies (7,9)] commonly exhibit significant interannual variations, which may be genuine or an artifact due to the model-measurement errors.The top-down CF 4 and C 2 F 6 emissions derived in this study are relatively close to a recently published bottom-up inventory of Guo et al. (8), especially in recent years when they agree within uncertainties, although the best estimates of Guo et al. (8) are still lower (e.g., ~6% relatively lower for CF 4 emissions and ~18% relatively lower for C 2 F 6 emissions in 2019) than the mean values of our top-down emissions.Our top-down emissions also agree relatively well with (but are still, on average, ~30% larger than) a bottom-up inventory for CF 4 emissions from the aluminum industry (9).The top-down CF 4 and C 2 F 6 emissions derived here are substantially larger than the bottom-up Emissions Database for Global Atmospheric Research (EDGAR) v8.0 inventory (20) (with the EDGAR inventory accounting for a range of only ~24-36% of   is generally less populated and less economically developed than other regions of China.However, this resource-intensive region has the largest aluminum production and contributes most to the aluminum production increase in China (Fig. 2 C and F).Both CF 4 and C 2 F 6 are formed as a by-product during anode effects in the aluminum industry and during continuous operations (16)(17)(18), likely explaining the substantial emissions in the northwest of China.The east of China also contributes significantly to national total C 2 F 6 emissions and their increases in China (Fig. 2 B and  E).The east of China is a highly populated and economically developed region with substantial aluminum production (Fig. 2 C and I), significant presence of semiconductor industry (gray triangles in Fig. 2 G and H and SI Appendix, Fig. S9A), and perhaps substantial flat-panel display industry (SI Appendix, Fig. S9B).
From the plots of emission spatial distributions (Fig. 2 G-I), emissions of CF 4 are located largely in the northwest regions of China, especially in recent years (SI Appendix, Figs.S4 and S5), and the distributions of CF 4 emissions are highly consistent with distributions of aluminum production.The spatial distributions of C 2 F 6 emissions (Fig. 2H) are similar to those of aluminum production (Fig. 2I), while some of the largest emissions of C 2 F 6 are colocated with semiconductor (Fig. 2 G and H and SI Appendix, Fig. S9A) and flat-panel display (SI Appendix, Fig. S9B) industries (e.g., around the Yangtze River Delta region in the east of China).The spatial distribution features of CF 4 and C 2 F 6 emissions imply that aluminum production is likely the dominant source of CF 4 emissions in China, including its significant contribution to emissions in the western regions of China, while aluminum production and the semiconductor (and flat-panel display) industry are both major sources of C 2 F 6 emissions, probably from different regions.This can also be validated to some extent by the comparison between emissions in this study with a previous bottomup inventory from the aluminum industry [bottom-up emissions from Kim et al. (9) in Fig. 1].The top-down emissions of CF 4 in this study are relatively close to the bottom-up CF 4 emissions from the aluminum industry (9), while the top-down emissions of C 2 F 6 in this study are much larger than the estimated bottom-up C 2 F 6 emissions from aluminum industry (9) (Fig. 1), which is expected given the relative industry contributions to CF 4 and C 2 F 6 emissions in China identified above.In this study, we do not explicitly discuss emissions from the rare earth metal industries due to the lack of industry data.However, the source processes of CF 4 and C 2 F 6 from the rare earth metal industry are similar to those of the aluminum industry, and the contribution from the rare earth industries to CF 4 and C 2 F 6 emissions may be relatively small (9,19), though more studies are needed.
The emission ratio of C 2 F 6 /CF 4 can provide insights into the origin of emissions in a specific region.This emission ratio is generally < 0.1 from the aluminum industry (and the rare earth metal industry) and > 0.4 from the semiconductor industry (including the flat-panel display industry) (9,10,17).The emission ratio in each subregion in each year is shown in Fig. 3. Emission ratios of C 2 F 6 /CF 4 in the northwest and southwest regions are among the lowest values and close to 0.1, suggesting that the CF 4 and C 2 F 6 emitted in these western regions originate mostly from the aluminum industry.The average emission ratio of C 2 F 6 /CF 4 in the east of China over the study period is 0.28, the highest among all the regions, and is between the reported emission ratios from the aluminum industry and semiconductor (and flat-panel display) industry, suggesting comparable contributions of the two industries to CF 4 and C 2 F 6 emissions in the east of China.
The substantial emissions of CF 4 and C 2 F 6 in the western regions likely result from the expanding aluminum industry in this resource-intensive area.Monitoring CF 4 and C 2 F 6 emissions in the western regions is necessary to evaluate the effectiveness of controls to reduce emissions of greenhouse gases and the achievement of carbon neutrality goals.Two previous long-term top-down emissions estimates of CF 4 and C 2 F 6 were derived by measurements made outside of China (7,9), specifically from the Gosan AGAGE site located in South Korea, which has lower sensitivity to emissions in regions beyond eastern China, e.g., the northwestern regions of China (SI Appendix, Fig. S10).Although the derived emissions from these previous top-down studies are relatively close to our top-down emissions (Fig. 1, ~24% relative difference on average over the overlapping years compared to this study for CF 4 emissions and ~11% relative difference for C 2 F 6 emissions), the two sites across the northwest of China used in this study (WLG and AKD, see Methods) likely provided additional information to constrain the spatial distributions of emissions.When comparing previous top-down emissions to those in this study, lower emissions were more commonly observed for CF 4 than C 2 F 6 (Fig. 1), which could be due to the substantial CF 4 emissions in the western regions emitted from the aluminum industries that were not well constrained by the measurements used previously.Without using the two sites in the northwest of China in this study (SI Appendix, Fig. S11), the derived emissions in China change by an average of ~12% over the period for CF 4 , and ~9% for C 2 F 6 , compared to emissions derived using all sites.Although excluding the sites in the northwest of China does not make a large difference to our conclusions drawn on the overall emission magnitudes and general trends in China, some of the spatial distribution information in the western regions would be missing if no sites in the northwest were used (SI Appendix, Fig. S11 C and D).
Emissions in China and Globally.Emissions of CF 4 and C 2 F 6 in China derived in this study are compared to global CF 4 and C 2 F 6 emissions (4) (Fig. 4 and SI Appendix, Table S1).The CF 4 and C 2 F 6 emissions in China and globally increased substantially between 2011/12 and 2020.Emissions in China represented a substantial fraction of global emissions during 2011 to 2020 and explained a major fraction of the global trend.Emissions of CF 4 from China were responsible for 42 (36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46) an increase of 3.2 (2.7-3.7)Gg y −1 was derived for CF 4 emissions in China between the two periods, compared to 3.1 (2.0-4.2) Gg y −1 globally].The increase in C 2 F 6 emissions in China is larger than the global C 2 F 6 emission increase between 2011-2012 and 2017-2020 [Fig.4D, an increase of 0.53 (0.46-0.61)Gg y −1 from China between these periods, compared to 0.29 (0.11-0.46)Gg y −1 globally], suggesting that C 2 F 6 emissions may have decreased elsewhere in the world over the same period.It is worth noting that the increases in C 2 F 6 emissions from China and globally are both highly uncertain.
The global C 2 F 6 /CF 4 emission ratio ranges between about 0.15 to 0.18, depending on the year, which closely mirrors the C 2 F 6 /CF 4 emission ratio of around 0.15 to 0.19 observed in China (SI Appendix, Fig. S12).This suggests that the relative size of emissions from different industrial sources of CF 4 and C 2 F 6 is similar in China and globally, with both the aluminum and the semiconductor/flat-panel display industries being the major contributors.The global C 2 F 6 /CF 4 emission ratios exhibit a decreasing trend over 2011 to 2020, indicating a global shift toward a greater dominance of aluminum industry in CF 4 and C 2 F 6 emissions compared to the semiconductor (and flat-panel display) industries and/or the gradual replacement of C 2 F 6 by alternatives such as NF 3 in certain cleaning processes in the semiconductor industries (9,32).The ratios of C 2 F 6 /CF 4 emissions in China show a large scatter with no discernable trend, perhaps in part due to the lack of data for several years (SI Appendix, Fig. S12).Primary aluminum production in China exhibits nearly identical growth patterns to the global total aluminum production over the last decade and contributes more than 90% to the global total increase in aluminum production (SI Appendix, Fig. S13).The dominance of China in the increase in global aluminum production is likely, in part, driving the global emissions growth of CF 4 and C 2 F 6 .The slowdown of the C 2 F 6 emission increase since 2018 in China and globally could be attributed to the emission reductions from the semiconductor/flat-panel display industries in recent years, where NF 3 is replacing C 2 F 6 in cleaning applications (9,32), while the lack of abatement equipment for destroying unused gases in these industries during the early years in China could be a potential explanation for the greater C 2 F 6 emission increase in China (best estimate) compared to the global total increase in C 2 F 6 emissions.Unfortunately, no further information from the semiconductor and flat-panel display industries is available to validate this hypothesis.

Discussion
Top-down emission estimates based on atmospheric observations can help to evaluate and improve national bottom-up inventory reporting and provide insights into spatial distributions and emissions source sectors.In this study, using long-term atmospheric observations of CF 4 and C 2 F 6 from a Chinese measurement network and an inverse modeling technique, we inferred rapid increases in CF 4  CF 4 emissions and ~38-64% of global C 2 F 6 emissions, depending on the year during the study period.The absolute increases in CF 4 and C 2 F 6 emissions in China are of similar magnitude (for CF 4 ) or larger than (for C 2 F 6 ) the absolute increases in global emissions over the same period, suggesting that changes in emissions in China may be the dominant driver behind changes in global CF 4 and C 2 F 6 emissions.We are also able to explore spatial distributions of the emissions sources.Substantial emissions of CF 4 and C 2 F 6 were determined to come from the less populated and less economically developed western regions of China.Emissions from these western regions of China are likely dominated by emissions from the aluminum industry, while the semiconductor and flat-panel display industries are likely to be considerable sources of emissions in the more economically developed regions, especially for C 2 F 6 .
The combined emissions of CF 4 and C 2 F 6 in China reached 78 Mt CO 2eq in 2021, which is equivalent to ~0.6-0.7% of the national total CO 2 emissions (20,33), ~4% of the national total CH 4 emissions (20) (using GWP 100 for CH 4 of 27.9), and around one-fifth of the national total N 2 O emissions (20) (using GWP 100 for N 2 O of 273) in 2021.The ongoing CF 4 and C 2 F 6 emissions in China, especially under the rapid expansion of China's aluminum and semiconductor industries, could offset progress toward China's carbon neutrality goal and global climate mitigation.There is a significant potential for reductions of CF 4 and C 2 F 6 emissions in China through technological innovation in the aluminum industry under different scenarios (34).The possibility of incorporating the aluminum industry into the carbon market in China (e.g., ref. 35) could stimulate the emission reductions of CF 4 and C 2 F 6 .It is important to continue to monitor emissions of CF 4 and C 2 F 6 in China, including in the resource-intensive western regions, to validate national inventory reporting and evaluate the effectiveness of emission mitigation controls.

Methods
To estimate CF 4 and C 2 F 6 emissions in China, we utilized the Numerical Atmospheric-dispersion Modelling Environment (NAME)-hierarchical Bayesian inference with Markov chain Monte-Carlo method (HBMCMC) regional inverse modeling framework that is described in detail in several previous papers (36)(37)(38)(39)(40).The framework has been used to estimate regional emissions from both in situ and flask measurements at multiple sites in a Chinese network (36,39,40).The approach consists of four parts: atmospheric observations, sensitivities of the observations to regional emissions, prior estimates of the emissions, and a Bayesian inference algorithm to solve for the emissions.S3).Weekly flask sampling was conducted at SDZ, WLG, LFS, XGL, AKD, XFG, and JSA, and daily flask sampling was conducted at JGJ.A mix of weekly and daily flask samples were collected at LAN (weekly before 2018 and daily after 2019).All the flask samples were analyzed at the CMA Beijing Laboratory.In addition to the above flask sampling, ~two-hourly in situ measurements were conducted at the SDZ station during 2011 to 2012 and 2016 to 2021.These measurements made at the above sites can in total provide good sensitivity to CF 4 and C 2 F 6 emissions across China (SI Appendix, Figs.S14 and S15).
AGAGE Medusa gas chromatographic system with mass spectrometric detector (41, 42) at the CMA Beijing Laboratory was used to analyze the mole fractions of CF 4 and C 2 F 6 from the flask samples and a second Medusa system was used for the in situ measurements at SDZ.The mole fractions are reported on the AGAGE SIO-14 calibration scale (43).The analyses of the samples were bracketed by analyses of the working standard gases to calibrate the measurements.The precision of the measurements is estimated to be 0.5% and 1% for in situ measurements and flasks for CF 4 , respectively, and 0.9% and 1.5% for C 2 F 6 .Further details regarding the sampling and measurements are available in previous studies (30,44,45).The high-frequency in situ measurements were averaged every 24 h in the inversion framework to reduce the influence of correlated model uncertainties between the successive samples.The observations for CF 4 and C 2 F 6 can be found in Datasets S2 and S3.The mole fractions that were input to the inversion framework after resampling are shown in SI Appendix, Figs.S16 and S17.
Sensitivities of the Observations to Emissions.To derive emissions based on the atmospheric observations, the quantitative relationships (sensitivities) between the observations and emissions are needed.The sensitivities of the atmospheric observations to emissions in each grid were estimated using a Lagrangian particle dispersion model, the UK Met Office NAME (46), which was run backward in time for 30 d to calculate the interaction of the air with the surface, and thus the sensitivity of the observations to surface emissions.Particles located within the lowest 40 m of the atmosphere above ground level were regarded as interacting with the surface emissions.The detailed configuration for the NAME runs can be found in previous studies (36)(37)(38)(39)(40).The output grid has a spatial resolution of ~0.234° in latitude and ~0.352° in longitude, within a computational domain between 5° S, 74° N, and 55° E, 192° E. These grids are aggregated into 150 regions by applying a quadtree algorithm (47) to the a priori contribution from each grid to the mole fractions.The 150 regions were used as the basic calculation units in the inverse modeling (basis functions).
The regional emissions are estimated based on the enhancements of the mole fractions over the background values (i.e., pollution events), so an estimate of background values is also needed.In the NAME-HBMCMC framework, the background is defined as the influence of all emissions outside of the computational domain as well as the well-mixed global background mole fractions.In the NAME backward run, the locations of particles leaving the computational domain were integrated to calculate the sensitivities of the observations to the background values.
Bayesian Inference Algorithm and Prior Emissions.A priori information is needed to estimate the emission values in the Bayesian inference algorithm (48,49).The a priori magnitudes for emissions were 5 Gg y −1 over China for CF 4 and 0.8 Gg y −1 for C 2 F 6 for all years, which are similar to the magnitudes of emissions in previous studies (7)(8)(9).The emissions were distributed in space using the intensity of lights at night, taken from the NOAA Defense Meteorological Satellite Program-Operational Line-Scan System (https://ngdc.noaa.gov/eog/data/web_data/v4composites/, last access: 1 March 2021), used here as a proxy for anthropogenic activity.The a priori emissions in each basis function were scaled during the inversion to optimally fit the observations, where the scaling factor was assumed to follow a lognormal distribution, with shape parameters mu of 0.2 and sigma of 0.8.This prior probability distribution ensures that the derived emissions are only positive.
The a priori magnitudes for background values were estimated by multiplying the NAME sensitivities to background values calculated above, by a background concentration field at the domain edges, which were taken from global AGAGE 12box model inversions (50,51).These estimates of background concentration field are based on the assimilation of monthly mean measurements at long-running background AGAGE sites.The calculated a priori background values were scaled during the inversion and are referred to as the boundary conditions in the framework.The prior probability distribution for the scaling factors of the boundary conditions was assumed to follow a lognormal distribution, with shape parameters mu of 1 and sigma of 1.A Markov chain Monte-Carlo method was used to solve the Bayesian inference equation, as explained in previous studies (36,37).
The number of available observations in each year of 2013 to 2015 for C 2 F 6 (as shown in SI Appendix, Fig. S17), is smaller than the number of basis functions (150), and there is only in situ data from SDZ available for CF 4 in 2011 (as shown in SI Appendix, Table S3).Due to the scarcity of data in these specific years, the emissions cannot be effectively constrained.Thus, only the emissions of CF 4 over 2012 to 2021, and the emissions of C 2 F 6 in 2011 to 2012 and 2016 to 2021 are discussed in this study, as shown in Fig. 1.

Fig. 1 .
Fig.1.Emissions of CF 4 and C 2 F 6 in China.Emissions of (A) CF 4 and (B) C 2 F 6 derived in this study using nine sites in China (black line with its shading representing the 68% uncertainty interval) are compared to previous top-down(7,9,(27)(28)(29)(30) and bottom-up(8,9,(20)(21)(22)(23)(24)(25)(26) emission estimates.Note that emissions of CF 4 and C 2 F 6 derived in this study are not available for some years during 2011 to 2021 for different experimental reasons (Methods).The years in the brackets after the legends of the previous studies are the time coverages of these studies.All available results during 2005 to 2021 were included in the plots for a complete comparison, although the previous emissions in early years do not overlap with the time coverage of this study and are not discussed.Some of the previous top-down studies were conducted using an interspecies correlation (ISC) method.The CF 4 and C 2 F 6 emissions derived in this study are tabulated in SI Appendix, TableS1and Dataset S1.

Fig. 2 .
Fig. 2. Emissions of CF 4 and C 2 F 6 and aluminum production in different subregions of China.The definitions of each subregion can be found in SI Appendix, Fig. S3.Panel (A) shows CF 4 emissions, (B) C 2 F 6 emissions, and (C) aluminum production in each subregion; panel (D) shows the increase in emissions of CF 4 between 2012 and the 2019-2021 average, (E) the increase in emissions of C 2 F 6 between the 2011-2012 and 2019-2021 averages, and (F) the aluminum production increase between the 2011-2012 average and 2019, from each subregion; panel (G) shows the spatial distribution of CF 4 emissions, (H) C 2 F 6 emissions and (I) aluminum production in each province (not including data for Hong Kong, Macao, Taiwan, and ocean regions).The uncertainties for CF 4 and C 2 F 6 emissions in plots (A and B) are the 68% uncertainty intervals.The values of subregional CF 4 and C 2 F 6 emissions can be found in Dataset S1.The sum of the percentages in plots (D-F) may be larger than 100%, because emissions/production in some of the regions may have decreased.The aluminum production data were obtained from the Yearbooks (31), with the subregional aluminum production in each year tabulated in SI Appendix, Table S2.Note that aluminum production data are only available during 2011 to 2019.The values shown in plots (G-I) represent the total annual quantities in each province averaged over 2016 to 2019, a period when emissions of CF 4 and C 2 F 6 and aluminum production data are all available.The gray triangles in plots (G and H) are the locations of semiconductor factories, obtained from Wikipedia (https://en.wikipedia.org/wiki/List_of_semiconductor_fabrication_plants,last access: 16 April 2023).The spatial distributions for all years can be found in SI Appendix, Figs.S4-S8.

Fig. 4 .
Fig. 4. Emissions of CF 4 and C 2 F 6 in China compared to global emissions.Panel (A) shows a comparison between the emissions of CF 4 and (B) C 2 F 6 in China derived in this study and the global emissions obtained from Laube and Tegtmeier (4) from AGAGE data.Multiyear averages were used to calculate the increase in emissions in China and globally.Panel (C) shows the changes in emissions between 2012 and the average of 2017-2020 for CF 4 and (D) between the average of 2011-2012 and the average of 2017-2020 for C 2 F 6 .

of Emissions of CF 4 and C 2 F 6 in China.
Table S1 and Dataset S1.the top-down CF 4 emissions derived in this study and ~14-20% of the top-down C 2 F 6 emissions in this study during 2011 to 2021) and the officially reported bottom-up emissions from China's national communications or biennial update to the UNFCCC (23-26) (with officially reported emissions accounting for a range of only ~34-41% of top-down CF 4 emissions in this study and ~23-28% of top-down C 2 F 6 emissions in this study over the overlapping years).The substantially lower bottom-up inventories compared to the top-down emissions indicate that these current bottom-up inventories are likely underestimated, in terms of the magnitudes of production of the relevant industries and/or the emission factors.We divide the emissions of CF 4 and C 2 F 6 in China into seven subregions, as shown in Fig.2.The northwest of China contributes the most to the national total CF 4 emissions in most years, a region for which previous top-down studies (7, 9) had low sensitivities.For emissions of C 2 F 6 , the northwest and east of China contribute most to the national total emissions among all the regions over the study period.We note large interannual variations in some subregional emissions, possibly due to the weaker constraint from the limited number of observations in these subregions.Thus, we used multiyear averaging over different periods to investigate the long-term trend of emissions in each subregion.Emissions from the northwest of China show substantial increases of 1.8 (1.4-2.1)Ggy−1 for CF 4 and 0.26 (0.22-0.30)Gg y −1 for C 2 F 6 between 2011-2012 and 2019-2021.The northwest of China contributed most to the national total emissions increase during this period (~49.1% of the national emissions rise for CF 4 and ~43.5% for C 2 F 6 ) (Fig.2 D and E).The northwest of China Spatial Distributions % of the global total CF 4 emissions in 2012, reaching 66 (59-71)% in 2020.Emissions of C 2 F 6 in China were responsible for 38 (33-42)% of the global total C 2 F 6 emissions in 2011, reaching 64 (58-70)% in 2020.The increase in CF 4 emissions in China is nearly equal to the global CF 4 emission increase between 2012 and 2017-2020 [Fig.4C, Emission ratios of C 2 F 6 /CF 4 in each subregion of China.Due to the lack of CF 4 emissions in 2011 and C 2 F 6 emissions in 2013 to 2015, only ratios in 2012 and over 2016 to 2021 are calculated.

Atmospheric Observations. Atmospheric
mole fractions of CF 4 and C 2 F 6 were measured at nine sites in China, as part of the China Meteorological Administration (CMA) network.The sites are located in different regions of China, namely Akedala (AKD, 47.10° N 87.97°E) and Mt.Waliguan (WLG, 36.29°N 100.90°E) in the northwest of China, Lin'an (LAN, 30.30°N 119.73°E) in the east of China, Longfengshan (LFS, 44.73° N 127.60°E) in the northeast of China,